Griffin, J;
Kalli, M;
Steel, M;
(2018)
Discussion of "Nonparametric Bayesian Inference in Applications": Bayesian nonparametric methods in econometrics.
Statistical Methods & Applications
, 27
(2)
pp. 207-218.
10.1007/s10260-017-0384-0.
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Abstract
The use of Bayesian nonparametrics models has increased rapidly over the last few decades driven by increasing computational power and the development of efficient Markov chain Monte Carlo algorithms. We review some applications of these models in economic applications including: volatility modelling (using both stochastic volatility models and GARCH-type models) with Dirichlet process mixture models, uses in portfolio allocation problems, long memory models with flexible forms of time-dependence, flexible extension of the dynamic Nelson-Siegel model for interest rate yields and multivariate time series models used in macroeconometrics.
| Type: | Article |
|---|---|
| Title: | Discussion of "Nonparametric Bayesian Inference in Applications": Bayesian nonparametric methods in econometrics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10260-017-0384-0 |
| Publisher version: | https://doi.org/10.1007/s10260-017-0384-0 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Statistics & Probability, Mathematics, Dirichlet process, Normalized random measures with independent increments, Volatility, Infinite mixture model, Interest rates, Portfolio allocation, Long memory, Time series, STOCHASTIC VOLATILITY, DYNAMIC-MODELS, RETURN, AGGREGATION, RATES |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10069570 |
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